



'''simple docstring'''





import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwiftFormerForImageClassification, SwiftFormerModel
 from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor


class 					snake_case			:



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_=13  ,UpperCAmelCase_=3  ,UpperCAmelCase_=True  ,UpperCAmelCase_=True  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=224  ,UpperCAmelCase_=1_000  ,UpperCAmelCase_=[3, 3, 6, 4]  ,UpperCAmelCase_=[48, 56, 112, 220]  ,)	->				List[str]:
           lowercase__									=    parent
           lowercase__									=    batch_size
           lowercase__									=    num_channels
           lowercase__									=    is_training
           lowercase__									=    use_labels
           lowercase__									=    hidden_dropout_prob
           lowercase__									=    attention_probs_dropout_prob
           lowercase__									=    num_labels
           lowercase__									=    image_size
           lowercase__									=    layer_depths
           lowercase__									=    embed_dims



       def 				_a   (					self   )	->				Tuple:
           lowercase__									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

           lowercase__									=    None
           if self.use_labels:
               lowercase__									=    ids_tensor([self.batch_size]  ,self.num_labels   )

           lowercase__									=    self.get_config()

           return config, pixel_values, labels



       def 				_a   (					self   )	->				Optional[Any]:
           return SwiftFormerConfig(
               depths=self.layer_depths  ,embed_dims=self.embed_dims  ,mlp_ratio=4  ,downsamples=[True, True, True, True]  ,hidden_act="gelu"  ,num_labels=self.num_labels  ,down_patch_size=3  ,down_stride=2  ,down_pad=1  ,drop_rate=0.0  ,drop_path_rate=0.0  ,use_layer_scale=UpperCAmelCase_  ,layer_scale_init_value=1E-5  ,)



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Optional[Any]:
           lowercase__									=    SwiftFormerModel(config=UpperCAmelCase_   )
           model.to(UpperCAmelCase_   )
           model.eval()
           lowercase__									=    model(UpperCAmelCase_   )
           self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.embed_dims[-1], 7, 7)   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Tuple:
           lowercase__									=    self.num_labels
           lowercase__									=    SwiftFormerForImageClassification(UpperCAmelCase_   )
           model.to(UpperCAmelCase_   )
           model.eval()
           lowercase__									=    model(UpperCAmelCase_  ,labels=UpperCAmelCase_   )
           self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)   )

           lowercase__									=    SwiftFormerForImageClassification(UpperCAmelCase_   )
           model.to(UpperCAmelCase_   )
           model.eval()

           lowercase__									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
           lowercase__									=    model(UpperCAmelCase_   )
           self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)   )







       def 				_a   (					self   )	->				Dict:
           ((lowercase__)							,							(lowercase__)							,							(lowercase__))									=    self.prepare_config_and_inputs()
           lowercase__									=    {"pixel_values": pixel_values}
           return config, inputs_dict





@require_torch
class 					snake_case			(UpperCamelCase		,   UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:Tuple     =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
       lowerCAmelCase__	:Optional[int]     =       (
           {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
           if is_torch_available()
           else {}
       )

       lowerCAmelCase__	:Any     =       False
       lowerCAmelCase__	:int     =       False
       lowerCAmelCase__	:Tuple     =       False
       lowerCAmelCase__	:str     =       False
       lowerCAmelCase__	:List[Any]     =       False



       def 				_a   (					self   )	->				str:
           lowercase__									=    SwiftFormerModelTester(self   )
           lowercase__									=    ConfigTester(
               self  ,config_class=UpperCAmelCase_  ,has_text_modality=UpperCAmelCase_  ,hidden_size=37  ,num_attention_heads=12  ,num_hidden_layers=12  ,)



       def 				_a   (					self   )	->				Optional[Any]:
           self.config_tester.run_common_tests()



       @unittest.skip(reason="SwiftFormer does not use inputs_embeds"   )
       def 				_a   (					self   )	->				List[Any]:
           pass



       def 				_a   (					self   )	->				Union[str, Any]:
           lowercase__							,							lowercase__									=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               lowercase__									=    model_class(UpperCAmelCase_   )
               lowercase__									=    model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(UpperCAmelCase_  ,nn.Linear   )   )



       def 				_a   (					self   )	->				Dict:
           lowercase__							,							lowercase__									=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               lowercase__									=    model_class(UpperCAmelCase_   )
               lowercase__									=    inspect.signature(model.forward   )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase__									=    [*signature.parameters.keys()]

               lowercase__									=    ["pixel_values"]
               self.assertListEqual(arg_names[:1]  ,UpperCAmelCase_   )



       def 				_a   (					self   )	->				int:
           lowercase__									=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_   )



       def 				_a   (					self   )	->				Tuple:
           lowercase__									=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_   )



       @slow
       def 				_a   (					self   )	->				Optional[int]:


           for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__									=    SwiftFormerModel.from_pretrained(UpperCAmelCase_   )
               self.assertIsNotNone(UpperCAmelCase_   )



       @unittest.skip(reason="SwiftFormer does not output attentions"   )
       def 				_a   (					self   )	->				int:
           pass



       def 				_a   (					self   )	->				str:


           def check_hidden_states_output(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   ):
               lowercase__									=    model_class(UpperCAmelCase_   )
               model.to(UpperCAmelCase_   )
               model.eval()

               with torch.no_grad():
                   lowercase__									=    model(**self._prepare_for_class(UpperCAmelCase_  ,UpperCAmelCase_   )   )

               lowercase__									=    outputs.hidden_states

               lowercase__									=    8
               self.assertEqual(len(UpperCAmelCase_   )  ,UpperCAmelCase_   )  # TODO

               # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
               # with the width and height being successively divided by 2, after every 2 blocks
               for i in range(len(UpperCAmelCase_   )   ):
                   self.assertEqual(
                       hidden_states[i].shape  ,torch.Size(
                           [
                               self.model_tester.batch_size,
                               self.model_tester.embed_dims[i // 2],
                               (self.model_tester.image_size // 4) // 2 ** (i // 2),
                               (self.model_tester.image_size // 4) // 2 ** (i // 2),
                           ]   )  ,)

           lowercase__							,							lowercase__									=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               lowercase__									=    True
               check_hidden_states_output(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               lowercase__									=    True

               check_hidden_states_output(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )



       def 				_a   (					self   )	->				Any:


           def _config_zero_init(UpperCAmelCase_   ):
               lowercase__									=    copy.deepcopy(UpperCAmelCase_   )
               for key in configs_no_init.__dict__.keys():
                   if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                       setattr(UpperCAmelCase_  ,UpperCAmelCase_  ,1E-1_0   )
                   if isinstance(getattr(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )  ,UpperCAmelCase_   ):
                       lowercase__									=    _config_zero_init(getattr(UpperCAmelCase_  ,UpperCAmelCase_   )   )
                       setattr(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )
               return configs_no_init

           lowercase__							,							lowercase__									=    self.model_tester.prepare_config_and_inputs_for_common()

           lowercase__									=    _config_zero_init(UpperCAmelCase_   )
           for model_class in self.all_model_classes:
               lowercase__									=    model_class(config=UpperCAmelCase_   )
               for name, param in model.named_parameters():
                   if param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1E9) / 1E9).round().item()  ,[0.0, 1.0]  ,msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''  ,)







       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."   )
       def 				_a   (					self   )	->				Dict:
           pass





def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
    return image



@require_torch
@require_vision
class 					snake_case			(unittest.TestCase ):



       @cached_property
       def 				_a   (					self   )	->				Any:
           return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"   ) if is_vision_available() else None







       @slow
       def 				_a   (					self   )	->				Any:
           lowercase__									=    SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"   ).to(UpperCAmelCase_   )

           lowercase__									=    self.default_image_processor
           lowercase__									=    prepare_img()
           lowercase__									=    image_processor(images=UpperCAmelCase_  ,return_tensors="pt"   ).to(UpperCAmelCase_   )

           # forward pass
           with torch.no_grad():
               lowercase__									=    model(**UpperCAmelCase_   )

           # verify the logits
           lowercase__									=    torch.Size((1, 1_000)   )
           self.assertEqual(outputs.logits.shape  ,UpperCAmelCase_   )

           lowercase__									=    torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]   ).to(UpperCAmelCase_   )
           self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,UpperCAmelCase_  ,atol=1E-4   )   )








import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__UpperCamelCase     		=							logging.getLogger()

__UpperCamelCase     		=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						os.makedirs(snake_case   ,    exist_ok=snake_case  )
						UpperCamelCase__       =							{"source": "What is love ?", "target": "life"}
						UpperCamelCase__       =							{"train": 12, "val": 2, "test": 2}
						for split in ["train", "test", "val"]:
									for field in ["source", "target"]:
												UpperCamelCase__       =							"\n".join([contents[field]] * n_lines[split]  )
												with open(os.path.join(snake_case   ,    F'''{split}.{field}'''  )   ,    "w"  ) as f:
															f.write(snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = "pytorch"  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_auto_remove_tmp_dir()
						UpperCamelCase__       =							os.path.join(snake_case   ,    "output"  )
						UpperCamelCase__       =							os.path.join(snake_case   ,    "data"  )
						self._create_dummy_data(data_dir=snake_case  )

						UpperCamelCase__       =							F'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

						if gpus > 0:
									testargs.append(F'''--gpus={gpus}'''  )
									if is_apex_available():
												testargs.append("--fp16"  )
						else:
									testargs.append("--gpus=0"  )
									testargs.append("--distributed_backend=ddp_cpu"  )
									testargs.append("--num_processes=2"  )

						UpperCamelCase__       =							[sys.executable, str(Path(finetune_rag.__file__  ).resolve()  )] + testargs
						execute_subprocess_async(snake_case   ,    env=self.get_env()  )

						UpperCamelCase__       =							os.path.join(snake_case   ,    "metrics.json"  )
						with open(snake_case  ) as f:
									UpperCamelCase__       =							json.load(snake_case  )
						return result




			@require_torch_gpu
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=1  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )




			@require_torch_multi_gpu
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=2  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )




			@require_torch_gpu
			@require_ray
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=1   ,    distributed_retriever="ray"  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )







			@require_torch_multi_gpu
			@require_ray
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=1   ,    distributed_retriever="ray"  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )


